TransmissionLab Version 1.3 available

A small update to TransmissionLab is available, which enables proper batch-mode operation and simplifies the command line acrobatics required for batch mode operation. This version is numbered 1.3, and is available either in source code format (from the Google Code Subversion repository) or as a binary JAR file release. The latter are found under “Downloads“, and include a matched JAR file, a ZIP file with library dependencies, and an example batch-mode parameter file.

Both the batch-mode parameter file and library dependencies have slight differences from Version 1.2, so be sure to grab both otherwise you’ll encounter errors starting up a simulation. In particular, this release adds a dependency upon the Jakarta Commons CLI library for command-line parsing, since this isn’t a strong suit of the Repast libraries.

This version also adds one statistic to the OverallStatisticsRecorder data collection module. For each simulation run, we calculate the average number of agents who have traits (measured at each model tick) which are listed in the “top N” list of traits. In other words, if you’re working with a “top 40” list of song-analogues, this statistic measures the number of agents whose chosen trait is a song in the top 40, as opposed to a trait that wasn’t frequent enough to make the top 40 list. This statistic is thus paired analytically with the parameter for the size of the “top N” lists, and the combination of the two should be interesting to examine across a range of mutation rate and population size parameters.

On a related note, LiveScience has an article on the upcoming article by Alex Bentley, Carl Lipo, Harold Herzog, and Matthew Hahn. I recommend it for a somewhat popularized account of the main conclusions of their 2007 paper. Since much of what we’re doing with TransmissionLab at the moment is going further along the lines suggested by Bentley et al., and earlier Fraser Neiman, Carl Lipo, and myself, it’s a good clue to the kinds of phenomena we can explore purely assuming that choice among alternatives is statistically random or neutral.